# estnoise: Estimate noise level In rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces

## Description

Estimates the noise level for a label vector 'y' and a denoised version of this label vector 'yh'. Which loss function is used to estimate the noise level depends on the kind of problem (regression problem or classification problem).

## Usage

 `1` ```estnoise(y, yh, regression = FALSE, nmse = TRUE) ```

## Arguments

 `y` a label vector containg only -1 and 1 for a classification problem, and real numbers in case of regression `yh` a denoised version of y which can be obtained by using e.g. rde `regression` FALSE in case of a classification problem, TRUE in case of a regression problem `nmse` if 'nmse' is TRUE and this is a regression problem, the mean squared error will be normalized

## Details

In case of a classification problem, the 0-1-loss is used to estimate the noise level:

y = (y_1, ..., y_n)

L\_01(y, yh) = (1/n)*sum(y != yh)

In case of a regression problem, the mean squared error (mse) or the normalized mean squared error (nmse) is used, depending on whether 'nmse' is FALSE (mse) or TRUE (nmse):

L\_mse = (1/n)*sum( (y - yh)\^2 )

L\_nmse = L\_mse(y, yh) / ((1/n)*sum( (y - (1/n)*sum(y))\^2 )

## Value

Estimated noise level

## Author(s)

Jan Saputra Mueller

`sincdata`, `rde_loocv`, `rde_tcm`, `rbfkernel`, `drawkpc`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## estimate noise of sinc data explicitly d <- sincdata(100, 0.7) # generate sinc data K <- rbfkernel(d\$X) # calculate rbf kernel matrix r <- rde(K, d\$y, est_y = TRUE) # estimate relevant dimension noise <- estnoise(d\$y, r\$yh, regression = TRUE) # estimate noise level ## estimate noise of sinc data implicitly (via rde_loocv) d <- sincdata(100, 0.7) # generate sinc data K <- rbfkernel(d\$X) # calculate rbf kernel matrix r <- rde(K, d\$y, est_y = TRUE) # estimate relevant dimension AND estimate noise r\$noise # estimated noise level ```

### Example output

```NULL
```

rdetools documentation built on May 2, 2019, 7:02 a.m.